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In your experience, which of the two models (all of OpenAI vs Gemini 2.5 Pro) are better for having as assistants to ask SWE/software systems related questions and doing long and complex reasoning?

I'm debating whether there's any point in paying for ChatGPT vs. paying (or even using the free version) of Gemini 2.5 Pro.

I have the feeling that most HNers prefer the latter, however in livebench I think OpenAI surpasses Gemini for coding.

There are so many AI projects with much overlap, different price tags and new ones released every day. I imagine choices will get more complicated over time(?) Is there a chat bot with up to date information some place? It seems like something that should exist.

Which IT certifications have the highest failure rates? And why do so many candidates struggle with them? From AWS to CompTIA, some exams consistently challenge even experienced professionals. In 2025, do coding certifications still hold value for developers, or has practical experience taken the lead?

I really like blogs, and I've started blogging again like this past week. I want to share what I write but get some nice reading lists going to.

Today I basically use HN as my blog curator, but I yearn for more.

Where do you find a blogging community nowadays? How to discover new blogs and how to share your content?

A demo where an AI agent buys an eSIM and pays using Bitcoin. The payment is made from a Euro balance using Lightning. No credit card,—just AI transacting in Bitcoin. Here’s what happens in the demo:

-> The AI picks the best eSIM plan (Rigged for the Demo) -> It makes a Bitcoin payment over Lightning -> It uses a Euro balance to send sats

This works using:

-> Lightning for instant and cheap transactions -> Bringin APIS exposed via MCP Model context protocol

Bitcoin is internet-native money. AI agents will need a way to transact, and Bitcoin is the best fit. But there are scaling issues with Bitcoin and being able to provide secure controlled access.

That's where Lightning and Nostr Wallet Connect come into the picture. They enable instant, cheap payments with controlled secure access to AI to make payments. Bitcoin adoption is not keeping up with AI development.

Most AI services are still stuck with fiat payment rails—slow, expensive, and restrictive. That’s where on/offramp providers should step in. Like how Bringin is doing.

I also wrote a blog post breaking down how Bitcoin can power AI payments. Check it out here: https://medium.com/bringin/bringin-bitcoin-to-ai-684e744b6795

I'm currently interviewing for new roles and while I did do some pretty cool work in my last role, I really struggle to talk about any of it in a remotely positive away. It's a period of my life where I was mostly unhappy and the endless arbitrary deadlines only compounded it, resulting in me staying there for several years too long just from feeling too busy to look at alternatives. While I don't think very highly of the company or upper-management, my disappointment and regret is mostly directed towards myself for not getting out of there years earlier.

Obviously complaining about the company or my personal situation at that time to a new prospective employer is an absolute no go. With how long I stayed it's virtually impossible to talk about older roles or just blitz my way through listing out the technologies I used; I have to talk about this one role, in detail, multiple times with every company.

Has anyone else had to deal with a similar issue? What kind of solutions did you come up with for it and have you done anything since to ensure you don't wind up in similar situations again.

I'm thinking of learning about compilers and am pleased to find that there seems to be at least two very accessible choices, "Writing An Interpreter In Go" and Crafting Interpreters. Curious if folks here have experience with either and could provide recommendations?

https://interpreterbook.com/ https://craftinginterpreters.com/

I recently wrote a blog walking through how to run WebAssembly (WASM) containers using containerd, crun, and WasmEdge inside a local Kubernetes cluster. It includes setup instructions, differences between using shim vs crun vs youki, and even a live HTTP server demo. If you're curious about WASM in cloud-native stacks or experimenting with ultra-light workloads in k8s, this might be helpful.

Check it out here: https://blog.sonichigo.com/running-webassembly-with-containe...

Would love to hear your thoughts or feedback!

(Note: I'm not linking the resulting book. This post focuses solely on the process and practical lessons learned collaborating with LLMs on a large writing project.)

Hey HN, I recently finished a months-long project collaborating intensively with various LLMs (ChatGPT, Claude, Gemini) to write a book about using AI in management. The process became a meta-experiment, revealing practical workflows and pitfalls that felt worth sharing.

This post breaks down the workflow, quirks, and lessons learned.

Getting Started: Used ChatGPT as a sounding board for messy notes. One morning, stuck in traffic, tried voice dictation directly into the chat app. Expected chaos, got usable (if rambling) text. Lesson 1: Capture raw ideas immediately. Use voice/text to get sparks down, then refine. Key for overcoming the blank page.

My Workflow evolved organically: Conversational Brainstorming: "Talk" ideas through with the AI. Ask for analogies, counterarguments, structure. Treat it like an always-available (but weird) partner. Partnership Drafting: Let AI generate first passes when stuck ("Explain X simply for Y"), but treat as raw material needing heavy human editing/fact-checking. Or, write first, have AI polish. Often alternated. Iterative Refinement: The core loop. Paste draft > ask for specific feedback ("Is this logic clear?") -> integrate selectively > repeat. (Lesson 2: Vague prompts = vague results; give granular instructions. Often requires breaking down tasks: logic first, then style). Practice Safe Context Management: LLMs forget (context windows). (Lesson 3: You are the AI's external memory. Constantly re-paste context/style guides; use system prompts. Assume zero persistence across time). Read-Aloud Reviews: Use TTS or read drafts aloud. (Lesson 4: Ears catch awkwardness eyes miss. Crucial for natural flow).

The "AI A-Team": Different models have distinct strengths: ChatGPT: Creative "liberal arts" type; great for analogies/prose, but verbose/flattery-prone. Claude: Analytical "engineer"; excels at logic/accuracy/code, but maybe don't invite for drinks. Gemini: The "copyeditor"; good for large-context consistency. Can push back constructively. (Lessons 5 & 6: Use the right tool for the job; learn strengths via experimentation & use models to check each other. Feeding output between them often revealed flaws - Gemini calling out ChatGPT's tells was useful).

Stuff I Did Not Do Well:

Biggest hurdles:

AI Flattery is Real: Helpfulness optimization means praise for bad work. (Lesson 7: Prompt for critical feedback. 'Critique harshly'. Don't trust praise; human review vital). The "AI Voice" is Pervasive: Understand why it sounds robotic (training bias, RLHF). (Lesson 8: Combat AI-isms. Prompt specific tones; edit out filler/hedging/repetition/'delve'; kill em dashes unless formal). Verification Burden is HUGE: AI hallucinates/facts wrong. (Lesson 9: Assume nothing correct without verification. You are the fact-checker. Non-negotiable despite workload. Ground claims; be careful with nuance/lived experience). Perfectionism is a Trap: AI enables endless iteration. (Lesson 10: Set limits; trust judgment. Know 'good enough'. Don't let AI erode voice. Kill your darlings).

My Personal Role in This fiasco:

Deep AI collaboration elevates the human role to: Manager (goals/context), Arbitrator (evaluating conflicts), Integrator (synthesizing), Quality Control (verification/ethics), and Voice (infusing personality/nuance).

Conclusion: This wasn't push-button magic; it was intensive, iterative partnership needing constant human guidance, judgment, and effort. It accelerated things dramatically and sparked ideas, but final quality depended entirely on active human management.

Key takeaway: Embrace the mess. Capture fast. Iterate hard. Know your tools. Verify everything. Never abdicate your role as the human mind in charge. Would love to hear thoughts on others' experiences.

Hi folks, I have built a pixel art game asset generation tool for game developers. However currently struggling to find early users. Can you suggest ways to attract users? Thanks.

Voice Flight is a browser game where players control a flying plane using just their voice. No login, no setup, just open and play.

Players control the plane using the loudness and duration of their voice. The louder the voice, the higher the plane flies.

https://voiceflight.vercel.app/

1、fastapi_mcp, open source, a simple way to convert：https://github.com/tadata-org/fastapi_mcp

2、Higress, open source, with a practical MCP market, powered by AI gateway：https://mcp.higress.ai/

3、RapidMPC: SaaS, no code, Unlimited MCP Servers, $12/month：https://rapid-mcp.com/

I’ve just stumbled upon RIAK. It seems like a very cool technology. Almost like an alternative to kubernetes. Has anyone used it in production? Why isn’t it more well known? It seems like an awesome solution.

I help startups roll out tools for their go-to-market teams. These days, I keep coming across different products with small teams, the backing of notable VCs, and lots of potential, but then when I go to use them the UIs are just littered with bugs which prevent them from functioning. And often it has nothing to do with prompting, hallucination, or anything like that. It's simple things like "when I hit save, my data disappears."

I'm accustomed to working with buggy tools, nothing I do is mission-critical, so things aren't as thoroughly tested as a car might be before hitting the road. But it seems things are getting released with more and more bugs. Am I nuts?

Seems like there are three possibilities to me:

1. This is just what happens with products that are new to market. 2. People creating these products are relying too much on tools like Cursor that don't work right. 3. The pressure to keep up is getting faster and faster, so companies are releasing products that are less and less thoroughly tested.

My gut tells me it's a combination of 2 and 3, and this is a sign we're reaching a new stage in the AI bubble. But maybe I'm wrong and being overly cynical.

I see at least one email or LinkedIn post a day that screams "written by AI". Using AI is fine, but if there’s no human fingerprint I drift off midway...same reaction as if I realize a cool photo is a fake.

Why do we spot the bot voice so fast?

* We riff off our audience. Models average everyone.

* Real people leave fingerprints...jokes that land, typos that don’t.

* Polished‑generic prose feels like a local news anchor gate‑crashing your group chat.

How I keep drafts human:

1. Spell out who's talking and why. "Support rep, one frustrated customer."

2. Paste the customer’s actual rant. Makes the model sweat. 3. Drop a paragraph in my voice, ask it to revise. Works better than rules.

4. Crank temperature a notch, then chop anything weird.

5. Read it aloud. If it sounds like corporate wallpaper, I rough it upswap a comma for an em dash, toss in a "whoops."

That’s my routine. What’s yours? I’m collecting tricks so my inbox stops looking like office ceiling drop in tiles.

Why can't devs make collaboration apps that are client native? I mean the technology exist to do decentralized collaboration already. I know most people would prefer an actual native program rather than a website/webapp. Heck some devs do this for their mobile app, but give a lower quality webapp for desktop users.

Hey!

I’m looking for a recent post (around 3-6 months old) in which grid animations are shown to demonstrate the process of heart muscle cell activation, and on a later stage, how a clump of dead cells can provoke a “vortex” and electrical reanimation is needed. I tried searching for it with Algolia but it’s not showing up.

Thank you in advance.

Hey - just wondering if anyone has any news of this poster. I see his handle many times when doing searches but the posting stopped abruptly on both reddit and here and I was just wondering if they are doing ok.

Big parts of our digital lives depend on our email address (personal, professional, financial…). I’m wondering how you've secured yourselves to prevent getting locked out of your email?

Of course, using my own domain is a no brainer, but I’ve heard horror stories of people getting kicked from their domains.

It feels like we are always at some company’s mercy, a mail provider, a domain service or something else.

I’m definitely overthinking, but it was triggered by a large financial institution that doesn’t support adding a recovery email.

I’m curious how you folks handle this, thanks!

Do you have a YouTube channel that gets around > 100 comments per video?

I'm looking to do some beta testing on a little platform I have built that does analysis on YouTube comments.

Totally free, happy for Feedback and will share the analysis on here, directly or via email.

Thanks in advance for anyone happy to spare a minute.

Are there any equivalent of Qt style loosely coupled "Signals and Slots" based JavaScript UI library? Qt allows widget/ components to be connected to each other with a pub-sub type of system where the emitter of the signal really need not care who the consumer is. While, IIUC, most JS libraries follow a hierarchically coupled state passing system where sharing of state happens through props/ passing down from parent to child components with lifted state etc., Was wondering if there are any JS libraries which operate in the style of Qt event driven signal-slot connections as a primary paradigm.

Hey HN community. I don't need prescription glasses, my eyesight is good. But I'm wondering if I need some sort of reflective glass anyways while I spent most of my day on the computer.

Do you use/recommend anything in particular? Thanks!

I am unsure if this is burnout, depression, totally normal or something else entirely.

Currently, I feel my productivity has diminished substantially. It's around ~20% what it used to be in 2016. I also procrastinate a lot more than I used to.

Much of the fire I had for development early in career has dwindled. It's not that I don't like it, I really do. There's no other activity I would rather do.

A lot of things happened from 2017 (laid off, child diagnosed with stage 4 cancer, mother died, sister died, and more).

On the bright side, after being laid off in 2017 my side project turned into an official business and I've been living off it ever since (pays ~2x more than a bay area salary). So "work" isn't really an issue.

I'd like to work more on my business and grow it. I'd like to also work on some side projects (maybe a game, some other business ideas, etc).

This sounds ridiculous to write, but I can't seem to do much when I sit down. Hours can go by and I haven't done much of anything.

Anyone experience this? Is this burnout? What can one do to fix this?

TL;DR: Cookie consent shouldn’t be a popup war on every website. Browsers should handle it natively — just like location or notifications — based on user-set privacy preferences. We can fix the web with one header, a little browser enforcement, and a lot less nonsense.

The current system for cookie consent is a mess. Every website throws a popup in your face, asking you to accept tracking you neither want nor need. The irony? It’s not technically necessary. We can solve it at the browser level — cleanly, universally, and in a user-respecting way.

Here’s how:

1. Browser-Level Privacy Preferences Browsers should allow users to set global consent preferences, just like setting a default language or search engine.

Example:

* Essential cookies: Always allow

* Analytics cookies: Ask or Block

* Marketing cookies: Ask or Block

* Third-party cookies: Ask or Block

Set once. Apply everywhere. No more popups.

2. New HTTP Header: Set-Cookie-Category Websites would categorize cookies when setting them, like:

Set-Cookie: sessionId=abc123; Category=Essential Set-Cookie: trackUser=true; Category=Marketing

Standardized categories: Essential, Analytics, Marketing, Personalization, Other. No trickery. No ambiguity.

3. Browser Enforcement When a site tries to set a cookie:

* Browser checks the declared category.

* Browser checks the user's privacy preferences.

* If no consent: cookie is silently blocked.

If consent is "Ask," the browser shows a small permission prompt (similar to location or notifications). No more hijacking the page UI.

1. Optional Website Messaging Websites could optionally trigger a browser-native dialog to explain their cookie use — but no walls of legalese blocking access.

2. Bonus: Easier Compliance Audits Browsers could expose APIs for compliance tools to automatically verify if a site respects consent preferences.

Why hasn’t this happened yet?

* Ad-tech companies make too much money off friction and dark patterns.

* Browser vendors (especially Chrome) profit from the status quo.

* Regulators targeted websites, not browsers, in GDPR/CCPA drafts.

But it’s not too late. Safari, Firefox, Brave, Arc — even Chrome (if enough pressure builds) — could easily implement this.

Users deserve better. The web deserves better.

If you think this should be built, upvotes help visibility.

I’m working on a product where the value proposition depends on users adopting a new behavior.

We know behavior doesn't shift just because something “could” be better. People stick to familiar tools unless there’s a clear incentive, a low-friction experience, or strong social proof. So we’re trying to be intentional about how we guide that change.

For those who’ve built products that asked users to do something new or uncomfortable:

What actually moved the needle for adoption?

Was it user education, incentives, or community-led momentum? Did you start with a small niche and expand?

Any frameworks or even personal mistakes you learned from? Would love to hear your stories, lessons, or anything you'd like to share. Thanks!

I've seen a number of comments characterizing homebrew, the package manager, as bad but it was usually in an offhand way.

The most annoying thing I've come across recently is that one can't downgrade to an old version (unless there's a specific formula). Htop 3.4. shows CPU usage 40x too small (on macOS) and to fix that I can't use brew.

The type of development I mean is GreaseMonkey or TamperMonkey scripts -- or even browser plugins -- that modify and "hack" web pages after they've loaded in the browser.

Is there a name to refer to this type of thing? Is this an accepted or pseudo-discipline? I'm trying to find best practices around this, but it's hard when there doesn't seem to be a nomenclature around it.

How are you cataloging, maintaining, versioning your prompts? Are you relying primarily on one toolchain where they are organizing by default in the toolchain interface (but locked in the toolchain)? Or are you using some other mechanism? Are you using a mechanism that enables you to share and permission them with team members, colleagues, friends?

The intent with this inquiry is to improve my own workflow(s) and potentially those of others interested in this topic. Thank you!

Curious to hear your most memorable Easter egg stories, whether in code, games, or real life.

I know that ~70% of embedded systems are programmed with C, lots of movement or at least motivation is seen on moving to Rust. My question is: why this languages are good for embedded software development? And overall what would constitute a good PL for this domain?